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This paper analyzes the effectiveness of public credit lines in promoting the
performances of Brazilian firms. We focus on the impact of the credit lines
managed by BNDES and FINEP in fostering growth measured in terms of
employment, labor productivity and export. For this purpose, we use a unique panel
data set developed by the Instituto de Pesquisa Econômica Aplicada (IPEA), which
includes information on both firm-level performances and access to public credit
lines. This particular data setting allows us to use quasi-experimental techniques to
control for selection bias when estimating the impact of the public credit lines. The
core of our estimation strategy is based on a difference-in-differences technique,
which we complement with matching methods for robustness check. Our results
consistently show that access to public credit lines has a significant and robust
positive impact on employment growth and exports, while we do not find evidence
of a significant effect on our measure of productivity. Interestingly enough, our
findings show that impact on exports is driven by the increase in export volumes
among exporting firms, while no significant effect on the probability of becoming
an exporter is detected.
Keywords: Public Credit, Impact Evaluation, SMEs, Difference in Difference, Panel Data,
Brazil, BNDES, FINEP.
JEL Classification: C23, H43, L25, O12, O54

1

We are grateful to Patrick Franco Alves for data management and Eduardo Pontual Ribeiro for very useful
discussions and comments on previous versions of this project. The findings and interpretations of the authors do
not necessarily represent the views of the Inter-American Development Bank. The usual disclaimer applies.
2
Director for innovation at the Financiadora de Estudos e Projetos (FINEP) and researcher at the Instituto de
Pesquisas Econômicas Aplicadas (IPEA), Brasilia, Brasil.
3
Lead Economist, Office of Strategic Planning and Development Effectiveness, Inter-American Development
Bank, Washington, DC. alessandrom@iadb.org
4
Economist, Office of Strategic Planning and Development Effectiveness, Inter-American Development Bank,
Washington, DC. cesarr@iadb.org
5
Research Fellow, Office of Strategic Planning and Development Effectiveness, Inter-American Development
Bank, Washington, DC. gvazquezbare@gmail.com

1

Table of Contents

1. Introduction

3

2. Discussion on Potential Impacts of Credit Programs

5

3. Public Credit Programs in Brazil

8

4. Data Description

10
11

4.1 Baseline Characteristics
5. Identification Strategy

17

6. Estimation Results

19

6.1 Full Sample Results

19

6.2 Construction of the Matched Sample

23

6.3 Matched Sample Results

27

6.4 Dynamic Effects of the Program

34

7. Conclusions

37

References

39

Appendix A. Construction of Variables

41

Appendix B. Balancing Tests

42

2
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1. Introduction
Public credit plays an important role in supporting the Brazilian productive sector. Data show
that the presence of the public sector in the banking sector is high. The largest state owned
development bank –the Banco Nacional do Desenvolvimento (BNDES)– accounted for 11
percent of all outstanding credit in 2006. Considering that the state also owns two of the three
largest commercial banks in Brazil, the percentage of outstanding credit accounted for stateowned banks increases to around 44 percent. Although the importance of the public sector in the
Brazilian financial system has been broadly debated, not much has been said on the effectiveness
of these policy instruments in improving the conditions of final beneficiaries of these resources.
This paper aims at shedding some light on the effectiveness of public credit programs in
promoting the performances of the productive sector in Brazil. In particular, we focus on the
impact of the credit lines managed by BNDES and FINEP in fostering growth measured in terms
of employment, labor productivity and export. For this purpose, we use a unique panel data set
developed by the Instituto de Pesquisa Econômica Aplicada (IPEA), which includes information
on both firm-level performance and access to public credit lines. This particular data setting
allows us to use quasi-experimental techniques to control for selection bias when estimating the
impact of the public credit lines. The core of our estimation strategy is based on a difference-indifference technique, which we complement with matching methods for robustness check.
Our results consistently show that access to public credit lines has a significant and robust
positive impact on employment growth and exports, while we do not find evidence of a
significant effect on our measure of productivity. Interestingly enough, our findings show that
impact on exports is driven by the increase in export volumes among exporting firms, while no
significant effect on the probability of becoming an exporter is detected.
The scope of this paper is mainly empirical and its contribution to the existing literature
should be considered in this context. This means that we do not develop any formal model aimed
at assessing the theoretical linkages between access to credit and the firm-level performances.
However, we complement our empirical analysis with a brief discussion of these linkages in light
of the existing literature. To put our paper into context, we also review the most recent impact
evaluations of public programs with objectives and means similar to the ones of the credit lines
we analyze.

3

The paper is structured as follows: after this introduction, section one provides a brief
review on the justification of public credit program aimed at fostering firm performances and on
the evidence that have been produced on the effectiveness of such programs. Section two
discusses more in detail the main characteristics of public credit programs in Brazil, with
particular emphasis on the credit lines managed by BNDES and FINEP. Section three describes
the data we are using for our analysis, including a review of the main basic statistics of interest.
Section four discusses our identification strategy, focusing on the approach we adopted to control
for selection biases. Section five presents the results of our estimations. Finally, section six
concludes and provides some policy recommendations.

4
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2. Discussion on Potential Impacts of Public Credit Programs
The fact that informational asymmetries generate credit constraints appears to be a consensus in
the literature at least since Stiglitz and Weiss (1981). In turn, the fact that financial constraints
may hinder firm performance has also been well-studied. For instance, poor access to financial
markets may negatively affect firm growth, especially among small firms (Beck, Demirgüç-Kunt
and Maksimovic, 2005). Rendón (2000) shows that capital market imperfections may restrict the
creation of permanent jobs, and remarks the importance of removing financial constraints to
promote job creation, particularly in economies with a high proportion of small firms. Moreover,
lack of access to credit may prevent firms from exporting, since this practice involves entry costs
related acquiring of information about foreign markets, customizing products to fit local tastes
and setting up distribution networks (Minetti and Zhu, 2010). Bellone et al (2010) claim that, in
this context, public intervention can help efficient but financially constrained firms to overcome
these fixed entry costs and expand their activities abroad.
Thus, in presence of financial constraints, public financing may be an effective
alternative to boost firm performance. In fact, several empirical studies show that public credit is
successful in relaxing financial constraints. For instance, Aivazian, Masundar and Santor (2003)
find that the World Bank’s Small and Medium Industries program in Sri Lanka led to a
relaxation of credit constraints and higher levels of investment for firms that received the
subsidies. This effect is, however, rather limited, not least because of the relatively small amount
of resources committed to this purpose.
Another finding of their analysis is that the public guarantee lowered the SMEs’
borrowing cost to a substantial extent. Banerjee and Duflo (2004) exploit the exogenous
variation generated by a policy change in India to test whether firms are credit constrained based
on their reaction to changes in directed lending programs. According to the authors, while both
constrained and unconstrained firms may be willing to absorb all the directed credit that they can
get, constrained firms will use it to expand production, while unconstrained firms will primarily
use it as a substitute for other borrowing. Their findings reveal that credit is used to finance more
production, which implies an increment in the rate of growth of sales and profits; this provides
evidence both on the existence of credit constraints and on the possibility of mitigate them
through public credit. Finally, Bach (2009) tests if the French loan program CODEVI succeeds

5

in improving access to credit for small French firms. The results show that access to the
financing subsidy substantially increased debt financing on the firm side. However, this did not
lead to significant substitution between subsidized and unsubsidized financing channels, which
can be taken as evidence of financial constraints.
As to the impact of credit programs on firm performance, to our knowledge, none of the
extant studies rely on experimental designs to evaluate this type of programs. Instead, the
literature focused on non-experimental techniques aimed at eliminating or at least mitigating
selection biases that are pervasive in this context since participation depends both on
administrative eligibility criteria and individual decisions of the firms. The most popular
approach, and the one used by all the evaluations described here, consists on applying differencein-differences methods to panel databases combined with propensity score matching techniques
to ensure the similarity between participants and non-participants.
Hall and Maffioli (2008) offer a review of empirical evaluations in Latin America.
According to the authors, studies reveal generally positive effects of credit programs on
intermediate outcomes like R&D expenditures, worker training and the introduction of new
processes and quality control practices, especially in developing countries (López Acevedo and
Tan, 2010). However, the evidence on the impact on longer-term performance outcomes like
sales growth, exports, employment, labor productivity and TFP is mixed. For instance,
Chudnovski et al (2005) analyze the FONTAR in Argentina, a program aiming at improving
R&D and technology development through matching grants. They find positive effects of 57 to
79% on innovation investment, but no significant impacts on labor productivity or new product
sales. Similarly, for the case of ADTEN, a subsidy program for R&D and technological
development in Brazil, De Negri et al (2006) find increased R&D expenditures by 50 to 90% but
no impact on sales, employment and labor productivity. Benavente, Crespi and Maffioli (2007)
study the Chilean FONTEC, designed to promote technology transfer and development and
R&D support. The authors estimate a 40% increase on sales growth and 3% increase on export
intensity, although they find no impact on labor productivity in Chile.
Building on this results, López Acevedo and Tan (2010) provide an evaluation of SME
credit programs in Mexico (Nafinsa, Bancomext, CONACyT, STPS and other programs from the
Ministry of Economy), Chile (SENCE, CORFO, PROCHILE, FONDEF), Colombia
(FOMIPYME) and Peru (BONOPYME, PROMPYME, CITE). The authors find positive gains in

6

sales, labor productivity and employment in Chile, and higher value added, sales, export and
employment in Mexico. In Colombia, the results suggest positive effects on exports, investment
in R&D and TFP. Finally, in Peru the findings show significant positive effects in sales and
profits. Confirming the findings of Hall and Maffioli, López Acevedo and Tan note that some of
the estimated impacts do not materialize until after several years. Thus, they claim that the lack
of impact of previous studies may be due to the short time dimension of the available databases,
and remark the importance not only of controlling for potential selection biases but also to
account for time lags to correctly estimate the effects of credit programs.

7

3. Public Credit Programs in Brazil
One important aspect of Latin American financial markets is the likelihood that firms are credit
constrained and rely too heavily on their own sources to finance investment (Galindo and
Schiantarelli, 2003, IDB, 2005). For instance, using data from The World Bank, approximately
25% of firms consider that they are credit constrained in Colombia. In Brazil, Bond, Soderbon
and Wu (2007) estimate that about 40% of firms are credit constrained using the same data from
2000-2003.
This has negative implications for aggregate investment levels. Various factors contribute
in generating credit constraints for MSMEs; from the demand side: their size, lack of collateral,
and their technical deficiencies to manage and/or implement sustainable investment projects.
From the supply side: limited medium- and long-term sources of funding in the domestic market
and lack of transparency and information to conduct proper credit risk assessments, leading to
reduced banksâ&#x20AC;&#x2122; appetite to serve this particular market segment.
Under this scenario, institutions such as BNDES in Brazil or BancĂłldex in Colombia,
with their access to domestic and foreign sources of medium- and long-term funding would most
certainly be easing credit constraints, improving investment levels and generating a more
efficient allocation.
The main objective of public credit programs is to support increased competitiveness and
job creation in (MSMEs) by channeling medium- and long-term financing for investments. The
Bank resources are added to those of development agencies or banks, commingled without
distinction, and disbursed through programs under their indirect operations system. IFIs must
comply with all Central Bank regulations and are responsible for evaluating the risk associated
with sub-borrowers and the decision to grant financing. Program funds will be used to finance
fixed investments or permanent working capital associated with the execution of investment
projects by qualifying MSMEs.
In Brazil, while BNDES is not the only source of public credit, it is by and large the one
with the biggest outlays for machinery and equipment acquisition: it accounts for 20% of all
credit demand in the economy and 5% of GDP. Many public banks, such as regional
development banks act only as financial intermediaries to BNDES, basically. The other two large
public banks, Banco do Brasil and Caixa, provide mainly agriculture credit and housing credit,

8
Â

respectively, as well as acting as financial intermediaries to BNDES. Furthermore, the
Financiadora de Estudos e Projectos (FINEP) is the Brazilian innovation agency and provides
public financing for research and development projects for the entire Science, Technology and
Innovation system.

9
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4. Data Description
For the purpose of this study, we rely on a unique dataset based on the combination of existing
administrative and statistical information 6 . Our final database is an unbalanced panel containing annual
firm level information from 1997 to 2007. The main source of information are two administrative
datasets: the Relação Anual de Informações Sociais (RAIS), which is an administrative file maintained by
the Brazilian Ministry of Employment and Labor (Ministério do Trabalho e Emprego, MTE), and the
Foreign Trade Dataset from the Secretariat of Foreign Trade (SECEX) of the Ministry of Development,
Industry and Foreign Trade (MDIC). RAIS has a universal coverage: all registered tax-paying
establishments must send every year to the Ministry information about every single worker who had been
employed by the establishment anytime during the reference year. The RAIS information provides a
matched employer-employee longitudinal data set, similar to those available in developed countries. The
data from SECEX provide information on the value of export of all Brazilian exporters for the same
period covered by RAIS. The two datasets were matched through a unique firm identifier number
(Cadastro Nacoinal de Pessoa Juridica, CNPJ).
The novelty of the RAIS data is the possibility to match the employer-employee structure with
detailed information available on workers' occupation, wages and schooling. So, the main use of RAIS
will be to provide the labor inputs variables. In addition, the SECEX data provides reliable information
regarding the value of total exports of firms. The coverage of the combined database includes all firms
that declare hiring workers in Brazil since 1996. For instance, in 2001, this represents more than 76
millions of workers declared in more than 230 thousand firms from a range of manufacturing types. The
panel data information allows classifying firms by activity, size, age of the firm and region of activity.
Finally, to capture the beneficiaries of public credit in Brazilian firms, we benefitted from a novel
database of public credit use collected by the Institute for Applied Economic Research (Instituto de
Pesquisa Econômica Aplicada, IPEA). This database has the foremost advantage of being able to crossreference the information using the CNPJ of each firm with other databases at the firm level in Brazil.
This information was available in an annual frequency from 1997 to 2007.
There are two main advantages of using a database with the characteristics described above. First,
the large number of observations (firms) makes statistically feasible to find firms that did not participate
in the program with similar characteristics to the ones that actually did participate (counterfactual).
Second, the panel data structure allows controlling for non-observable effects that determine program
participation and firm performance. Nevertheless, the main disadvantage is that RAIS database does not
have information regarding total sales, and hence, it is not possible to construct total factor productivity

6

The details and definitions of the variables used appear in the Appendix I.

10

(TFP) measures. Still, it can be argued that total salary expenditure and total exports have a close
relationship with firmsâ&#x20AC;&#x2122; TFP. Formally, from basic production theory, real wages are a measure of labor
productivity. Under this hypothesis, evaluating the impact of the program in terms of average real wages
would be an approximation of the impact in terms of labor productivity. Nevertheless, there are also
arguments that challenge this view. For instance, the existence of collective wage agreements, special
benefits for years worked in the firm or efficiency wages. To deal with a more precise measure of real
wages we construct a synthetic measure of average standardized wages that represents an approximation
of labor productivity at the firm level. Annex I describes the construction of this variable.
Given the nature of the data and the fact that public credit programs have been in place since
before 1997 we needed to make a decision regarding which year should be considered as the starting
point for our analysis. In other words, these programs have been in place for years before the first year of
the sample we have -1997-, and are still active throughout the entire sample.
In order to evaluate the effectiveness of such intervention we need to consider an alternative
starting point for those programs. This decision is far from trivial and inevitably involves some
discretionarily, but such simplification, if something, should go in the direction of underestimating the
long-run effect of the use of public credit. Assuming this caveat and its consequences we therefore
decided to consider 2001 as the alternative starting point of the use of public credit in Brazil mainly based
on a statistical argument. Thus, all the firms that enter the program before or after 2001 are excluded from
the analysis. The decision is based on the fact that the year 2001 divides the sample evenly such that it
maximizes the statistical power of the analysis by placing an equal number of years before and after the
chosen starting year. Needless to say, we understand our results as a first and therefore preliminary
analysis of the impact of such program.
4.1 Baseline Characteristics
Table 1 shows that in 2001, public credit use comprises almost 17 thousand firms of which 23% were
exporters. Almost a third of the beneficiary firms are producers of food and plastic, mainly concentrated
in the south and southeast region. The vast majority -80%- of such firms are micro and small sized.

11
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Table 1. Main Characteristics of Public Credit Recipients in 2001
Treated
Number Distribution
16,700
100%

In Table 2 we present summary statistics for the outcomes and covariate variables before
the beginning of the program in 2001, for beneficiaries and non-beneficiaries of public credit. It
can be seen that beneficiaries have systematic greater magnitudes in all variables (employment,
total wage expenditure, total exports, total imports, total age of the firm, average credit size and
average standardized wage) and their difference with non-beneficiaries is strongly significant. 7
The information presented here is consistent with the previous table and is giving
evidence suggesting that firms that enter the program are larger in size, they spend more in
wages, they export and import more, they are older, they take more public credit and they have a
higher average standardized wage than the rest. In fact, this could be reflecting the presence of
unobserved factors affecting the participation decision. The identification strategy, to be
explained below, will take into consideration these issues to find appropriate control firms and
avoid biases generated by these unobserved factors.

7

Appendix I present a description of the variables used and its construction.

Furthermore, when inspecting the trends of the main outcomes (exports, employment, and profits
per worker) before the starting of the program –between 1997 and 2000-, it can be seen that there is a
different behavior between treated and non-treated firms. Figures 1 to 3 show pre-treatment trends
behavior for exports, employment and average standardized wage. Although at first sight the pretreatment performance may look alike between treated and non-treated firms, when performing a test of
equality of trends the null hypothesis of equality is rejected. 8 This divergent performance could be due,
among other factors, to the fact that the non beneficiaries are a very heterogeneous group of firms and
may not constitute an accurate comparison group for treated firms. To analyze the impact of the program
in such setup will require finding an appropriate counterfactual to the treated firms. This will be the first
task of the identification strategy.

8

Tables available upon request to Alessandro Maffioli (alessandrom@iadb.org).

14

Figure 1. Exports in Logs (Before Matching)

4

Ln(exports)

3

2

1

0
1997

2000

2003

2006

Year
Treated

Not treated

2

Ln(employment)
2.5
3
3.5

4

Figure 2. Employment in Logs (Before Matching)

1997

2000

2003
Year
Treated

15

Not treated

2006

-.15

-.1

Renda_pa
-.05

0

.05

Figure 3. Labor Productivity (Before Matching)

1997

2000

2003
Year
Treated

16
Â

Not treated

2006

5. Identification Strategy
We will use the group of non-participating firms to estimate the counterfactual outcome of the
treated firms, i.e. to calculate what would have the outcome been for treated firms, had they not
been treated. However, as the previous section suggests, the pool of untreated firms is not
necessarily comparable to the group of beneficiaries, since the intervention is not randomly
assigned and hence potential issues of self-selection and administrative selection bias arise which
can seriously compromise the validity of the estimations.
Several techniques can be used to avoid these potential problems. We will use two
methods to deal with selection bias, namely, standard fixed-effects regressions and a
combination of fixed effects and propensity score matching.
First, if participation is determined by observable factors, these variables can be included
as control variables in a regression framework. However, some of these relevant factors may be
unobservable (for instance, entrepreneurial behavior of the firm, manager characteristics, etc),
and thus cannot be accounted for. Nevertheless, the panel structure of our database allows us to
eliminate all unobservable factors, as long as they do not vary with time, using a fixed-effects
model.
More rigorously, we propose the following specification:
(1)

where

is the outcome of the firm i in year t,

the outcome and are firm-specific,

captures all time-constant factors that affect

represents yearly shocks that affect all firms,

is a binary

variable that takes the value one since the year in which the firm i enters the program,
vector of time-varying control variables and
uncorrelated with

is a

is the usual error term assumed to be

. The standard errors will be clustered at the firm level for the inference to

be robust to within-firm correlation of the error terms. In absence of time-varying unobserved
factors that affect both the outcome and the participation, the fixed-effects method leads to
consistent estimator for , the impact of the program.
The validity of the difference-in-differences (fixed-effects) estimator rests on the
identification assumption that trends in the outcomes would have been equal in absence of
treatment. However, this assumption may be difficult to accept when firms in the control group
are very heterogeneous and very different from the participating firms, since firms that are very

17
Â

different are likely to follow different trends as well. In order to reinforce the results, we also run
equation (1) on a matched sample, selecting among the firms in the comparison group those that
are more similar to beneficiaries not only in terms of observed characteristics but also on their
pre-treatment performance. We do this to ensure that we selecting from the control group only
those which have pre-treatment trends that are similar to those in the treated group.
More precisely, we define the year previous to treatment as a baseline year and estimate
the propensity score, i.e. the conditional probability of participation, using a probit model:
1|

,

for a fixed pre-treatment year t, where
outcome variable,

,…

Φ
is a vector of covariates,

(2)

is a vector of k lags of the

, and Φ is the standard Normal cumulative distribution

function.
We use a different probit for each outcome. Since the main objective of the matching is
to ensure that ex-ante trends are similar between groups, we argue that running separate probits
for each outcome is a more flexible strategy to find appropriate matches for each treated firm;
this is so because, for instance, a comparison firm may be a good match for a treated firm in
terms of ex-ante trends in exports but may follow a different dynamic in employment. Therefore,
running separate probits allows finding better matches for each outcome. The main disadvantage
of this choice is that the resulting control groups are different for each outcome, which may
complicate the comparison of the results across outcomes. However, considering the importance
of the similarity of trends for the validity of the estimations, we believe that the advantages of
this choice outweigh its costs.
Using the predicted probability of participation, we match each treated firm with the
untreated firm with most similar propensity score; we then drop from the database all the
control-group firms that are not matched to any treated firm and run equation (1) on this matched
subsample.

18

6. Estimation Results
Results will be presented firstly for the full sample as a whole and then only focusing on the
common support that will be explained and constructed below.
6.1 Full Sample Results
This section summarizes the results obtained by estimating equation (1) using the fixed effects
estimator for the three outcomes of interest: employment (in logs), total exports (in logs) and
labor productivity. The participation variable is a dummy that takes the value of 1 once the firm
started participating in the public credit program.
Table 3 shows the impact of the program in employment. The dependent variable is the
total number workers expressed in logarithms. Column 1 shows a strongly significant and
positive effect of around 23% 9 when only controlling for time dummies. Column 2 shows that
this effect is robust to the inclusion of control variables; the coefficient increases to 25%. Finally,
column 3 includes industry-year interaction terms, which allow for differential time trends across
industry sectors. The results are indistinguishable from the ones in column 2. Hence, although
the effect of the program decreases as we control for observables, it does not differ significantly
by adding control variables. When interpreting these impacts we need to take into account the
trajectories of the control and the treated group throughout the period of analysis. At the
baseline, the matched sample of treated firms exhibit on average 100 employees per firm, hence
a 23% increase implies an increase of 23 employees for the treated firms with respect to the
control group.

9

More precisely, since the treatment variable is binary and the outcome is measured in logarithms, the correct way
to interpret the coefficient is to calculate exp(b)-1. However, the “raw” coefficient is in most cases a very close
approximation to the discrete impact, and hence we use what we consider the more straightforward way of
interpreting the results.

Table 4 shows the impact of the program in exports. The dependent variable is total
exports expressed in logarithms. This variable is constructed in such a way that if a firm has no
exports in a given year, a value of one is assigned. This procedure allows us to construct a
logarithmic version of total exports with no missing values. Through this mechanism, the
interpretation of the impact of the program in this variable is the same as the logarithm of
employment. Column 1 reveals a strongly significant positive impact of 47% on exports when
controlling for time dummies. The estimated impact decreases after the addition of control

20
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variables, but remains large and significant (39%). This effect is robust to the inclusion of
industry-year interaction terms.
Table 4. Impact on Exports (Full Sample)

Table 5 shows the impact of the program in labor productivity. According to the first set
of estimations, none of the specifications detect a significant impact. This lack of impact might
seem counterintuitive. A word of caution is needed here. In this specific case, the lack of impact
could be also related to way we are approximating labor productivity, i.e. through real wages,
because real wages may change slower than real labor productivity.

6.2 Construction of the Matched Sample
One possible concern with the previous estimations is that the control group is very
heterogeneous and thus not necessarily comparable to the treated firms. To reinforce the validity
of the results, we need to select from the control group a subgroup of firms that are much more
similar to treated firms in terms of observable characteristics.
More precisely, we use matching techniques to pair each treated firm to the most similar
untreated firm. We do this in two steps. We first estimate a probit model for the propensity score
(i.e. the conditional probability of participation) for each firm using a vector of observed
characteristics as predictors. We then match each beneficiary with the untreated firm with more
similar propensity score, and we run the previous results on this new matched sample, dropping
all the untreated firms that are never used as a comparison.
The probit model is run on the year previous to treatment to ensure that none of the
predictors are affected by the intervention. In addition to standard control variables like age and
industry sector, we also include several lags of the outcome variable to match not only on the
values of observable characteristics but also to ensure that treated and control firms followed
similar paths before treatment. As described in the methodological section this is a necessary
condition for the difference in difference (fixed effects) estimator to be consistent. In particular,
we run three different probit models to perform separate analyses for each outcome. In each one
of these, we use four lags of the corresponding outcome variable to capture pre-treatment trends,
plus a set of control variables as shown in tables 6 to 8.
The results of the probit models for 2001 are presented in table 6. The dependent variable
is dichotomous and takes the value of one if the firm borrowed from either BNDES or FINEP in
2001.

From each probit model, we can conclude that the oldest firms with the most skilled
workers and the highest wage expenditures have a higher probability of participating in the
public credit program. Similarly, compared to the largest firms, smaller ones have more
probability of participating in the program. This information is consistent with the summary
statistics described above and gives evidence of a participation bias. In other words, we need to
control for this selection bias to be able to attribute to the program the difference in outcomes
between treated and non-treated firms. If we leave this issue unattained, the difference in
outcomes may be given by the pre-treatment difference between treated and non-treated.
With the probit models estimates, we predict the probability of participation and match
each beneficiary with the non-beneficiary with closest propensity score. We construct this
control group using the one-nearest-neighbor algorithm. Finally, we drop from our sample all the
control firms that are not matched to any treated firm. (See figures 4 to 6).

2

2.5

Ln(employment)
3
3.5

4

Figure 4. Employment (Matched Sample)

1997

2000

2003
Year
Treated

25
Â

Not treated

2006

0

1

Ln(exports)
2

3

4

Figure 5. Exports (Matched Sample)

1997

2000

2003

2006

Year
Treated

Not treated

-.02

0

Renda_pa
.02
.04

.06

.08

Figure 6. Labor Productivity (Matched Sample)

1997

2000

2003
Year
Treated

26
Â

Not treated

2006

Tables A1 to A3 (see Appendix II) show the balancing test for the covariates included in
each participation equation considering a control group defined by the matching procedure. In
fact, after the matching, the hypothesis of equality of means of observable characteristics for
both treated and untreated firms cannot be rejected. In sum, both the graphical evidence and the
statistical tests suggest that the matching is successful in constructing a control group that is very
similar to the treated group. Once these characteristics (including the pre-treatment trends)
between participating and non-participating firms are balanced, the common support defined is
free from selection bias and we can attribute the difference to the program participation. Thus,
we can now proceed to run the previous regressions in this new matched sample.
6.3 Matched Sample Results
Tables 7 to 10 present the results of the estimation over the common support. In general, the
results for the matched sample are very similar to the ones for the full sample. The estimated
impact on employment is again around 24% and around 40% for exports, while we find no
significant impact on average standardized wages.

One potential concern with the results for exports is that the estimators are mixing two
distinct possible effects: on the one hand, the program may increase export volumes, but also
change the pool of exporting firms by inducing firms to start exporting. To address this issue, we
perform two separate analyses. First, we study the impact of the program on the probability of a
firm being an exporter using as the outcome of interest a binary variable that takes the value one
if the firm has non-zero exports. To estimate this specification we use a linear probability model.

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Such model has some limitations with respect to its close-related probit or logit, mainly the fact
that marginal effects are constant 10 . Nevertheless, it has the advantage of straightforwardly
controlling for fixed effects. The results of these estimations are presented in table 10.
Table 10. Impact on Probability of Exporting (Matched Sample)

Another drawback of the linear probability model is that it does not guarantee that the predicted probability to be
between zero and one, although this is irrelevant in this case where the estimates are not used for prediction

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In principle, it can be argued that firms that move from non-exporter to exporter are those
that were able to overcome credit constraint and access international markets. In other words,
those firms that were affected by the program and increase their productivity by moving to
international markets are the most productive ones. As the table shows, we find no significant
impact on the probability of exporting. This finding suggests that the positive impact found in the
previous estimations must be mainly driven by the increase in export volumes among firms that
were already exporting.
To further test this hypothesis, we now study the effect of the program on export volumes
by restricting the sample to firms that were already exporting in the two years previous to
treatment. These results are presented in table 11.

The findings reveal very large and significant impacts, supporting the hypothesis that the
effect on exports is almost entirely driven by the increase in export volumes among exporting
firms, while not affecting the probability of becoming an exporter.
In sum, the results for the matched sample confirm the previous findings; we estimate
positive and large impacts on employment and exports, but not for the average standardized

33
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wages. Moreover, the impact on exports is driven by the increase in export volumes among
exporting firms.
6.4 Dynamic Effects of the Program

While the previous results estimate the average impact for the whole post-treatment period, we
now set the attention on analyzing the dynamic pattern of these effects. In other words, the
interest is to disentangle the effect of the program to understand if those effects are constant or
vary over time.
We modify our econometric specification by replacing the treatment variable with a
dummy variable

that takes the value one in the first year of treatment and zero otherwise.

Also we will use several lags of this variable,

, each one of those indicating the impact of

the intervention in the k-th year of treatment. Table 12 shows the results for the three outcomes
of interest.

The impact on employment is always significantly positive and increases with time. In
figure 7 these effects are depicted with its corresponding standard error. It is clear the strong and
significant positive trend that the treatment has.
Figure 7. Dynamic Impact on Employment
0.5
0.4
0.3
0.2
0.1
0
1

2

3
4
Years since treatment
95% confidence interval

5

6

In the case of exports, the impact of the program seems to take longer to appear in the
regressions, since the coefficient for the first dummy is insignificant. The dynamic impact
exhibits an inverted U-shape, increasing during most of the years but slightly decreasing in the
last one. This pattern is displayed in figure 8 and although this evidence is preliminary it
suggests the existence of an optimal duration of the treatment.
Figure 8. Dynamic Impact on Exports
1
0.8
0.6
0.4
0.2
0
1
‐0.2

2

3

4

Years since treatment
95% confidence interval

36

5

6

7. Conclusions
The main objective of this paper was to provide evidence on the effectiveness of public credit
line in Brazil. We find that access to public credit lines has a significant and robust positive
impact on employment and exports, while we do not find evidence of a significant effect on our
measure of productivity. Interestingly enough, our findings show that impact on exports is
mainly driven by the increase in export volumes among exporting firms, while we do not
significant effect on the probability of becoming an exporter.
These results suggest that the second-tier public credit system effectively foster firmsâ&#x20AC;&#x2122;
growth and, more specifically, it helps exporters to maintain and increase their operations, while
they do not provide conclusive evidence of productivity gains. Some caution is probably needed
when interpreting this lack of effect on productivity: in this case, the result may be more related
to the specific indicator we are using rather than to a real lack of impact. In fact, one would
expect a simultaneous increase of export and employment to be accompanied by improvements
in productivity. Unfortunately, due to data limitation, we could not compute our preferred
measure of productivity, TFP, and, therefore, we have to acknowledge that our results remain
inconclusive in this particular aspect.
Because of the relevance and size of the state-owned Banks in Brazil, our findings offer a
valuable contribution to the debate on which policy instruments should be used to support the
development of a competitive productive system in emerging countries. Sound and wide access
to credit has always been considered a key ingredient of any private sector development strategy.
Our results show that the provision of credit through second-tier development banks in Brazil
play a significant role in making credit available for firms and effectively improve firms
competitiveness, in particular when measured in terms of volume of exports.
However, as in the case of most empirical studies, a good dose of caution is needed when
impact evaluation findings are used in a debate on the alternative use of public resources. First,
although they are consistently robust under our specification, one should carefully consider the
external validity of our results. We limit our analysis to a set of credit line managed by two key
state-owned development Banks (BNDES and FINEP). Therefore, our results only reflect the
effectiveness of these institutions. Expand their significance to other sources of public credit in
Brazil or other countries would require a set of well define interpretative assumptions. Second,
because of methodological reasons we have focus on a particular cohort of credit recipients. The

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level effectiveness of public program could potentially depend on external factors that may vary
other time. The period we considered may have been particularly problematic for private lenders
and, therefore, we cannot exclude that the positive effects of the public credit lines we observe
may depend on the that particular conjuncture. Finally, we are not able to complement our
impact analysis with a similarly robust assessment of the efficiency of the credit lines we
consider. Providing credit at certain condition could be quite costly for the public budgets. These
costs should be more than compensated by the value of the benefits we observed in order to
concluded that this specific use of public resources as a valuable return for the Brazilian society.
This paper also contributes to the methodological debate on how to evaluate the
effectiveness of programs aimed at supporting firm-level performances. In particular, we show
how to take advantage of a data setting that not only allow us to reduce selection bias by
controlling for firm level fixed effects, but also to further improve the credibility of the
difference-in-difference assumption by matching treated and comparison groups on the pretreatment trends of the outcomes variables. Because administrative dataset with similar
characteristics to the one we used are becoming more and more available, our estimation strategy
may be replicated to evaluate similar programs in other emerging countries.
These contributions notwithstanding, further research is certainly still need in this area.
First, as explained before, our preferred measure of productivity, TFP, could not be computed
with the data available for this study. Microdata on TFP are potentially available in Brazil,
though only for manufacturing firms. A first extension of this study will consider this measure.
Second, future research should expand the analysis beyond average treatment effects. With
access to more detailed information about the characteristics of the credit lines, we could analyze
the heterogeneous effects that access to public credit line may have depending on loan terms,
targeted firms populations and other specific requirements of the credit lines. Third, future
research should also focus on better understanding the relationship between credit conditions and
performances. For this purpose, one should be able to not only control for firm-level pretreatment economic performances (which under reasonable assumptions could be consider a
good proxy of a firmâ&#x20AC;&#x2122;s financial health), but also for the firm-level financial characteristics. This
kind of data are more complicated to construct, but they are potentially available in financial
systems with a certain level of supervision and they could provide a key contribution to a better
understanding of mechanism through which public credit lines affect firm-level performances.

Appendix I. Construction of Variables
PUCR: dichotomous variable that takes the value of one if the firm borrowed from either
BNDES or FINEP in 2001.
Employment: firm's total employment is constructed by counting the number of records in the
PIS RAIS, weighting these counts by the number of months the employee was hired at the firm.
For instance, an employee remaining employed throughout the year counts as being equal to 1,
while if it remained employed for six months in the year it counts as 0.5. Thus this variable
actually reflects the number of jobs provided by the firm during the year.
Exports: total value in U.S. dollars (U.S. $ FOB) of export transactions per firm in each year.
This information was obtained through the sum of all operations into a single total exports per
firm per year.
Labor Productivity: it is obtained through the difference between income and average income
of the employee in the sector (CNAE4), in the unity of the federation (UF) and the class of
personnel actions of the firms hired, according to the expression
Wpad i j l k m =

Wi j l k m − W j l k m
STD (W i j l k m )

where W i j l k represents the wage of the i-th employee in the j-th firm in the l-th location in the k-th sector
of economic activity within the m-th size category. After the standardization of the average income of the
employee, the averages are calculated for each firm, according to the expression:
PO

Wpad i j l k m

i =1

PO j l k m

Wpad j l k m = ∑

The above information represents a measure of labor productivity at the firm level.

the impact of public credit programs on brazilian firms

this paper analyzes the effectiveness of public credit lines in promoting the performances of brazilian firms. we focus on the impact of the credit lines managed by bndes and finep in fostering growth measured in terms of employment, labor productivity and export. for this purpose, we use a unique panel data set developed by the instituto de pesquisa econômica aplicada (ipea), which includes information on both firm-level performances and access to public credit lines.